Health care mines big data to predict trends, set policies

By ERIC STEINKOPFF,
July 15, 2013 at 8:00 AM

The team at St. Luke’s University Health Network has come a long way since the health care organization was established by the Moravians in the late 1800s.

It is unlikely its founders dreamed of things such as computers to store data and even less likely that the information could be manipulated to predict which patients might need extra attention during their visit.

But that is what is happening.

“We can provide quality low-cost care to the community,” said Amanda Mazza, director of analytics and business intelligence at St. Luke’s University Network. She has been in her current job seven years and in the health care industry for about 16 years.

“It’s a very exciting time to be in my role. There’s such energy,” Mazza said. “It’s like solving puzzles all day.”

Gone are the days when one structured database might contain simple things such as a patient’s name, address and telephone number.

Today there are multiple sources of unstructured information such as patient charts, medication records and treatment protocols that can be merged together in what the industry calls “big data” – and then compared for trends both inside and outside the particular hospital. It’s a trend that is taking hold at St. Luke’s and nationwide as the industry copes with ever-changing regulations and an ever-changing demographic.

“Heath care is big data,” Mazza said.

The high volume, high speed and high complexity of health care information make it more technically oriented than financial records or retail inventory and sales reports.

“There are probably 200 systems we could pull data from, and not all of it is structured,” Mazza said.

The traditional data management skills for entering and maintaining information are only the foundation. Today the field of analytics defines how expert digital number crunchers merge the information into usable formats to make changes in health care.

Some of the information is entered into a computer system when someone is registered for an appointment, visits the hospital, is prescribed medication or even gets an X-ray, Mazza said.

“But we’re mostly interested in performance improvement,” she said.

So additional data also are available for scrutiny, such as the clinic type and location, financial information, surgeries or procedures, medications including antibiotics, infections, readmission to the hospital, specific doctors or specialists, rehabilitation, follow-ups, lab results, test results, type of disease or condition.

The team typically uses data up to five years old but can go back 10 years if needed.

“All of that information is available for analysis,” Mazza said. “We aggregate it, summarize it and benchmark it.”

Once the personal information is stripped off the files and is compiled into a usable format, trends can be compared in the benchmark stage to other health care facilities outside of St. Luke’s.

People such as Mazza have been reporting those trends to the business leaders at the hospital – who then make appropriate decisions on reducing costs and improving care long before the Patient Protection and Affordable Care Act known as Obamacare was a reality.

“The technology and business imperatives are coming together,” Mazza said. “We have a very robust performance improvement program that keeps costs down and quality up.”

Some of the obvious signs of these initiatives are computerized documentation through computers on wheeled carts so health care providers can input data near the patient.

There are bar code scanners on arm bands and medications that measure drug dosage, order and timing, leading to fewer medication errors.

“These are checks and balances. The doctor orders, the pharmacy provides and the nurse administers,” Mazza said. “Patient safety is huge. Safe care means fewer complications, fewer readmissions and less work lost.”

According to Mazza, initially it was enough to make sure data management was accurate and accessible, and then it was sufficient to provide decision-makers with management tools such as daily, weekly, monthly or annual reports.

But now they are in the business of advanced analytics called predictive modeling.

“This is a new era – predictive modeling – leveraging more of the data to provide opportunities to improve care,” she said. “If five patients come in with the same condition, which one is likely to be a readmission? So he or she needs supportive care now.”

This is “identifying risk right at the point of care,” Mazza said.

Members of the hospital staff are focusing on three very high-risk types of patients, those suffering from heart failure, heart attack and pneumonia.

Specifically, those with heart failure have a readmission rate to the hospital as high as 28 percent.

“Basically, one in four are coming back,” said Jennifer Sprankle, director of clinical quality improvement who has been with St. Luke’s for 11 years and in her current position for about four years. “Some will be readmissions regardless” but “there are preventable readmissions, and those are the populations we can go after.”

According to Sprankle, when these patients check out of the hospital, they are seen by a nurse specially trained in their particular ailment. Early follow-up visits are scheduled, home phone calls and home visits could be ordered – as well as high-tech home equipment that can monitor blood pressure, heart rate, weight and other vital signs and automatically transmit the information back to the hospital.

Data managers such as Sprankle hope that manipulating large volumes of information will help them someday identify who needs special attention and where there is room to improve patient care.

“It’s very much in the infancy stage,” she said. “This predictive modeling is a relatively new concept for health care.”

All of this data manipulation might raise eyebrows for those concerned about personal information falling into the wrong hands, but there are procedures that go well beyond requirements of the Health Insurance Portability and Accountability Act.

In addition to computer passwords, firewalls and antivirus software, there are mandatory background checks – for those who use the data – for substance abuse, personal records, criminal records and psychological tests.

“It’s always top of mind, especially anything patient identifiable,” Mazza said. “You don’t share it if you don’t have to. You do not drop the ball on privacy.”

The predictive models used already are free of any identifying personal information in much the same way that a case study in a textbook might refer to an anonymous group of patients.

“If we do that well, the rest takes care of itself,” Mazza said. “We need to learn as a nation how to maximize our IT [information technology] investment [and] how to improve health care.”